Method and system for measuring speed of moving target based on Wi-Fi signal

Abstract

The invention provides a moving target speed measurement method and system based on a Wi-Fi signal, relates to the technical field of wireless sensing, and aims to solve the problems that the prior art cannot overcome the problems of noise interference and signal attenuation in a complex environment, is not suitable for multi-scene speed measurement and is low in speed estimation method precision. The method comprises the following steps: acquiring a Wi-Fi dynamic signal caused by a moving target; an automatic gain control value and a Doppler frequency spectrum are extracted; calculating a shadow fading parameter according to the automatic gain control value; dividing a Doppler spectrum, and calculating a multi-scale speed; detecting a shadow fading area according to the shadow fading parameter; and judging whether the moving target is in the shadow fading area, if so, reconstructing the attenuation speed to obtain the reconstructed scale speed, and fusing the reconstructed scale data and the non-reconstructed scale data to obtain the speed of the moving target. According to the method, the precision and robustness of speed estimation are improved, the problems of noise interference and signal attenuation in a complex environment can be effectively solved, and the method is suitable for multi-scene speed measurement.

Description

Technical Field

[0001] The present invention belongs to the field of wireless sensing technology, and in particular relates to a method and system for measuring the speed of a moving target based on Wi-Fi signals. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of intelligent Internet of Things technology, the importance of wireless sensing technology in various application scenarios such as smart home, smart medical care, and autonomous driving has become increasingly prominent. Especially in the field of measuring the speed of target objects, traditional methods usually rely on wearable devices, visual sensors, radars or ultrasonic sensors. However, these methods have problems such as complex equipment deployment, high cost, and susceptibility to environmental conditions, and cannot meet the low-cost and high-precision requirements in diverse scenarios. In recent years, non-contact speed measurement technology based on Wi-Fi signals has gradually become the mainstream research direction and research hotspot in this field due to its advantages such as strong equipment versatility, good privacy protection, and wide application range.

[0004] As a ubiquitous wireless communication signal, Wi-Fi signals can propagate in complex environments and penetrate obstacles. When the target object moves, the Wi-Fi signal will change in amplitude, phase, and frequency due to reflection, diffraction, and Doppler effect. These changes contain rich motion information. Through signal processing and spectrum analysis, the dynamic features related to the target object's motion can be extracted and its speed can be estimated. In particular, Doppler spectrum analysis technology can accurately capture the signal frequency deviation caused by the target motion, providing important support for speed measurement.

[0005] Wi-Fi signals show great potential in the field of motion speed perception, but still face many technical challenges, which are mainly reflected in the following three aspects:

[0006] (1) The impact of noise interference on signal quality. When using Wi-Fi signals for speed measurement, the signal will be significantly interfered by the surrounding environment during propagation. For example, static or dynamic multipath reflection signals from walls, furniture, and other people will be superimposed on the signals of the target object to form a complex signal mixing environment. This complex signal environment increases the difficulty of extracting effective information from Wi-Fi signals, which seriously affects the accuracy of speed estimation. In addition, Wi-Fi devices themselves also have inherent noise problems. For example, when commercial Intel 5300 network cards collect Wi-Fi signals, there are problems such as center frequency offset, sampling frequency offset, and message detection delay, which further reduce signal quality. Therefore, before performing speed measurement, environmental noise and device noise must be eliminated through effective signal preprocessing technology to ensure signal quality and the reliability of speed estimation results.

[0007] (2) Insufficient scenario applicability. When the target object passes through the shadow fading area, the Wi-Fi signal is prone to strong attenuation due to the serious impact of the target object on the direct, reflected and diffracted signals in the signal propagation process. This signal attenuation not only reduces the signal quality, but also leads to a significant decrease in signal perception performance, making the speed estimation unstable and inaccurate. Therefore, how to describe the motion characteristics of the target object in the shadow fading area, accurately reconstruct the signal and compensate for the attenuation has become the key to improving the accuracy and robustness of the speed measurement system.

[0008] (3) Traditional speed estimation methods are not accurate enough. Different moving targets have significantly different effects on Wi-Fi signal propagation. In particular, during motion, the reflection and diffraction characteristics of the signal are closely related to the relative position and speed of the target object. However, traditional Wi-Fi-based speed estimation methods usually only calculate speed by extracting spectrum energy peaks, ignoring the different spectrum responses of different objects during motion. This simplified estimation method cannot fully reflect the dynamic characteristics of the moving target in the spectrum. Therefore, how to extract speed features of different scales based on the Doppler spectrum and accurately characterize the motion characteristics of the target object through speed dynamic weight fusion technology is another major challenge to improve the accuracy and robustness of speed measurement.

[0009] In summary, environmental noise, multipath effects and hardware attenuation can significantly reduce signal quality, resulting in reduced reliability and accuracy of speed measurement results. In addition, the diversity of target objects and the complexity of scenes make it difficult for existing technologies to be applicable to multiple scenes at the same time. Therefore, how to overcome environmental interference and enhance the robustness and accuracy of the speed measurement system has become a key issue that needs to be solved in this field. Summary of the invention

[0010] In order to overcome the shortcomings of the above-mentioned prior art, the present invention provides a method and system for measuring the speed of a mobile target based on Wi-Fi signals. Through key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading parameter estimation, and multi-scale speed fusion, the accuracy and robustness of speed estimation are significantly improved, and the noise interference and signal attenuation problems in complex environments can be effectively dealt with. It is suitable for speed measurement of various target objects and provides a new solution for the application of wireless sensing technology in the intelligent Internet of Things.

[0011] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0012] A first aspect of the present invention provides a method for measuring the speed of a moving target based on a Wi-Fi signal, comprising:

[0013] Acquire dynamic Wi-Fi signals caused by moving targets;

[0014] Extract automatic gain control value based on Wi-Fi dynamic signal caused by moving targets;

[0015] Calculate shadow fading parameters according to the automatic gain control value;

[0016] Extract Doppler spectrum based on the dynamic Wi-Fi signal caused by moving targets;

[0017] Divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0018] Detect shadow fading area according to shadow fading parameters;

[0019] Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed. If it is not in the shadow fading area, no reconstruction is performed and the scale speed is not reconstructed.

[0020] The reconstructed scale data and the unreconstructed scale data are fused using a dynamic weighted velocity fusion algorithm to obtain the velocity of the moving target.

[0021] As an implementation method, the Wi-Fi dynamic signal caused by the moving target is obtained, and the specific process is as follows:

[0022] Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal, and extract the subcarrier with large dynamic response based on the subcarrier performance parameters;

[0023] The dynamic signal ratio method is used to perform hardware noise suppression on subcarriers with large dynamic response;

[0024] Use a Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression;

[0025] The signal after Gaussian noise suppression is processed by adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

[0026] As an implementation method, the signal after Gaussian noise suppression is processed by adaptive filtering, and the specific process is as follows:

[0027] Perform linear interpolation on the signal suppressed by Gaussian noise to obtain a continuous suppressed signal;

[0028] The linear phase technique is used to keep all frequency components of the continuous suppression signal with the same time delay;

[0029] The Butterworth bandpass filter is used to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise;

[0030] Wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by the moving target.

[0031] As an implementation method, the automatic gain control value is extracted, and the specific process is: using the Python or MATLAB script of the channel state information tool to parse the automatic gain control value.

[0032] As an implementation method, the Doppler spectrum is extracted, and the specific process is as follows:

[0033] The main components are extracted from the Wi-Fi dynamic signals caused by moving targets through singular value decomposition technology;

[0034] The Doppler spectrum is extracted from the principal components using short-time Fourier transform.

[0035] As an implementation method, it is determined whether there is shadow attenuation in the multi-scale velocity. If there is shadow attenuation, the attenuation velocity is reconstructed. The specific process is as follows:

[0036] Calculate the decay speed of multi-scale velocities in the presence of shadow decay;

[0037] The decay velocity is reconstructed by folding the multi-scale velocity data with shadow attenuation.

[0038] As an implementation method, a speed fusion algorithm with dynamic weights is used to fuse the reconstructed scale speed and the unreconstructed scale speed to obtain the speed of the moving target. The specific process is as follows:

[0039] Calculate multi-scale velocity characteristic parameters;

[0040] Calculate the correlation between scales of multi-scale velocities;

[0041] Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively;

[0042] According to the reconstructed multi-scale velocities and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

[0043] A second aspect of the present invention provides a mobile target speed measurement system based on Wi-Fi signals, comprising:

[0044] Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets;

[0045] A shadow fading parameter estimation module is used to extract an automatic gain control value based on a Wi-Fi dynamic signal caused by a moving target; and calculate a shadow fading parameter according to the automatic gain control value;

[0046] The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the Wi-Fi dynamic signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0047] The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area, if it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed, if not, no reconstruction is performed, and the scale speed is not reconstructed;

[0048] The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

[0049] As an implementation method, the data acquisition module includes a transmitting end and a receiving end for sending and receiving signals.

[0050] As an implementation mode, both the transmitting end and the receiving end are provided with a collection system platform based on a channel state information tool, and the collection system platform is set to a Monitor mode.

[0051] One or more of the above technical solutions have the following beneficial effects:

[0052] In this embodiment, through key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading area detection and reconstruction, the challenges brought by environmental noise, hardware interference and multipath effect are effectively overcome, the signal quality and the reliability of speed estimation results are improved, and the high accuracy and strong robustness of speed measurement are ensured. At the same time, it is suitable for speed measurement of various target objects, providing a new solution for the application of wireless sensing technology in the intelligent Internet of Things.

[0053] In this embodiment, based on the shadow fading area detection and the motion characteristics of the target object in the shadow fading area, the signal is accurately reconstructed and the attenuation is compensated, thereby improving the accuracy and robustness of the speed measurement system.

[0054] In this embodiment, velocity features of different scales are extracted based on the Doppler spectrum, and the motion characteristics of the target object are accurately characterized through the velocity dynamic weight fusion technology, which improves the reliability and applicability of velocity estimation and is suitable for a variety of target objects and complex scenes.

[0055] In this embodiment, there is no need to deploy additional dedicated equipment, and the existing Wi-Fi environment is fully utilized, which greatly reduces the system deployment cost.

[0056] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0058] Figure 1 This is a schematic diagram of the Wi-Fi device used in the first embodiment;

[0059] Figure 2 This is a flow chart of a method for measuring the speed of a moving target based on Wi-Fi signals according to the first embodiment of the present invention;

[0060] Figure 3 This is a schematic diagram of multi-scale Doppler division and multi-scale velocity estimation in the first embodiment of the present invention;

[0061] Figure 4 This is a rendering of the shadow fading area of ​​the first embodiment;

[0062] Figure 5 This is an effect diagram of speed reconstruction with shadow fading in the first embodiment;

[0063] Figure 6 This is a diagram showing the effect of speed fusion calculation in the first embodiment. DETAILED DESCRIPTION

[0064] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0065] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0066] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0067] Embodiment 1

[0068] This embodiment discloses a method for measuring the speed of a moving target based on Wi-Fi signals.

[0069] To more clearly illustrate this embodiment, a process for implementing the speed measurement of a moving target based on a Wi-Fi signal can be specifically described as follows:

[0070] A method for measuring the speed of a moving target based on a Wi-Fi signal, comprising:

[0071] S1. Acquire the Wi-Fi dynamic signal caused by the moving target;

[0072] S2, extracting an automatic gain control value based on a Wi-Fi dynamic signal caused by a moving target;

[0073] Calculate shadow fading parameters according to the automatic gain control value;

[0074] S3. Extract Doppler spectrum based on Wi-Fi dynamic signals caused by moving targets

[0075] Divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0076] S4. Detecting the shadow fading area according to the shadow fading parameters;

[0077] Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed. If it is not in the shadow fading area, no reconstruction is performed and the scale speed is not reconstructed.

[0078] S5. Use a dynamic weighted speed fusion algorithm to fuse the reconstructed scale data and the unreconstructed scale data to obtain the speed of the moving target.

[0079] like Figure 1 , Figure 2 As shown, in step S1, a Wi-Fi dynamic signal caused by a moving target is acquired.

[0080] S1-1. Collect Wi-Fi dynamic signal data.

[0081] In this embodiment, Wi-Fi transmitters and receivers are placed on both sides of the target object motion area for sending and receiving signals. In order to fully release the perception capability of the transceiver devices, both transceivers are equipped with an acquisition system platform based on the channel state information tool CSI TOOL, and the acquisition system platform is set to Monitor mode. In Monitor mode, the transmitter device can stably send data packets at a high transmission rate. In order to improve the accuracy of speed estimation, the signal transmission rate of the transmitter device is set to 1000Hz. Use Python and Matlab to extract and process the Wi-Fi CSI data received in the data packet.

[0082] After the above steps, the Wi-Fi dynamic signal data is collected, which provides a data basis for subsequent extraction of Wi-Fi dynamic signals related to mobile targets, multi-scale spectrum analysis, shadow fading area detection and reconstruction, and other operations.

[0083] S1-2. Extract the Wi-Fi dynamic signal caused by the moving target.

[0084] In this embodiment, the Wi-Fi dynamic signal related to the mobile target is extracted from the collected Wi-Fi dynamic signal. The specific process is as follows:

[0085] (1) Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal and extract the subcarrier with large dynamic response based on the subcarrier performance parameters;

[0086] In this embodiment, each transceiver link includes 30 subcarriers. In order to filter subcarrier data that is least affected by noise and can better reflect the motion characteristics of the target object, it is necessary to filter the subcarrier with the maximum dynamic response according to the subcarrier performance parameter. The calculation formula of the subcarrier performance parameter is:

[0087]

[0088] Where γ represents the subcarrier performance parameter, represents the average value of 30 subcarriers, and σ represents the standard deviation of 30 subcarriers.

[0089] The larger the γ is, the better the subcarrier dynamic performance is. According to formula (1), the maximum γ value is calculated from 30 subcarriers. When the γ value is the largest, the dynamic response is the largest. The smaller the parameter is. It is a comparison between subcarriers. By comparing the size of this parameter between subcarriers, the maximum value can be found.

[0090] After the above steps, the subcarriers with greater dynamic response to the moving target are screened out, providing a data basis for subsequent operations.

[0091] (2) Using the dynamic signal ratio method to perform hardware noise suppression on subcarriers with large dynamic response;

[0092] In this embodiment, the commercial Intel 5300 network card has problems such as center frequency offset, sampling frequency offset and message detection delay when collecting Wi-Fi signals. However, these hardware noises are constant on different antennas of the same device. The dynamic signal CSI ratio method is used to effectively eliminate the influence of the above hardware noise. The CSI ratio method formula is:

[0093]

[0094] Among them, CSI afteratio (t) represents the CSI data after CSI ratio is calculated. ant1 (t) represents the CSI data of the first antenna, CSI ant2 (t) represents the CSI data of the second antenna.

[0095] After the above steps, the impact of hardware noise can be effectively reduced, the signal quality can be improved, and a data basis can be provided for subsequent operations.

[0096] (3) using a Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression;

[0097] In this embodiment, a Gaussian filter is used to perform an additive Gaussian noise elimination operation on the signal after hardware noise suppression to obtain a Gaussian noise suppressed signal.

[0098] (4) The signal after Gaussian noise suppression is processed through adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

[0099] In this embodiment, the influence of discontinuity or jitter caused by missing data on subsequent signal processing operations is eliminated, and the signal after Gaussian noise suppression is linearly interpolated to obtain a continuous suppressed signal.

[0100] To prevent phase distortion, linear phase techniques are used to keep all frequency components of the continuous suppression signal with the same time delay.

[0101] In order to obtain a cleaner spectrum diagram with the dynamic characteristics of the target object, a Butterworth bandpass filter is used to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise.

[0102] In order to obtain cleaner frequency features at multiple frequency scales, wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by moving targets.

[0103] After the above processing, the Wi-Fi dynamic signal caused by the moving target is expressed as:

[0104]

[0105] Where i represents the number of subcarriers, k represents the number of data packets, and f i represents the frequency of the ith subcarrier, c is the speed of light, Δt is the sampling time interval, v is the moving speed of the target object, β i,k is the amplitude response.

[0106] like Figure 2 As shown, in step S2, an automatic gain control value is extracted based on the Wi-Fi dynamic signal caused by the moving target; and a shadow fading parameter is calculated according to the automatic gain control value.

[0107] S2-1. Extract automatic gain control value.

[0108] In this embodiment, the automatic gain control value AGC is extracted from the Wi-Fi dynamic signal related to the mobile target, specifically: the automatic gain control value is parsed using the Python or MATLAB script of the channel state information tool. The automatic gain value is an inherent parameter in the physical layer and is obtained through the parsing code provided by the channel state information tool CSI TOOL.

[0109] S2-2. Calculate shadow fading parameters according to the automatic gain control value.

[0110] In this embodiment, the shadow fading parameter is calculated according to the automatic gain control value AGC, and the formula is:

[0111]

[0112] Among them, SFA(t) is the time corresponding to the window w d Standard deviation of internal AGC, S t+j is the AGC value at time t+j, S t+j is the AGC value at time t; SFP(t) is the shadow fading parameter.

[0113] In this embodiment, the threshold is set to 1. When SFA(t) is greater than the threshold, the shadow fading parameter SFP at this moment is equal to 1; when SFA(t) is less than the threshold, the shadow fading parameter SFP at this moment is equal to 0.

[0114] After the above steps, it is possible to effectively determine whether the target object has entered the shadow fading area and achieve accurate detection, which provides a basis for subsequent velocity reconstruction and fusion.

[0115] like Figure 2 As shown, in step S3, based on the Wi-Fi dynamic signal caused by the moving target, the Doppler spectrum is extracted; the Doppler spectrum is divided into multiple scales, and the multi-scale velocity is calculated.

[0116] S3-1. Extract Doppler spectrum based on the Wi-Fi dynamic signal caused by the moving target.

[0117] In this embodiment, the Doppler spectrum is extracted, and the specific process is as follows:

[0118] (1) The principal components of the Wi-Fi dynamic signals caused by moving targets are extracted through singular value decomposition technology.

[0119] In this embodiment, the singular value decomposition (SVD) technology is used to extract the principal components in the CSI data, retain the main features in the signal, and perform dimensionality reduction to reduce the computational complexity and storage requirements.

[0120] (2) Use short-time Fourier transform to extract the Doppler spectrum from the principal component.

[0121] In this embodiment, the Doppler spectrum of the CSI data is calculated using the short-time Fourier transform (STFT) technology in the extracted principal component space.

[0122] In order to improve storage efficiency and reduce the amount of calculation, the Doppler spectrum is compressed and the multi-dimensional matrix is ​​simplified.

[0123] After the above steps, the system's robustness to interference signals in complex environments is significantly improved, while the amount of calculation is reduced and the calculation efficiency is improved.

[0124] S3-2. Divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity.

[0125] In this embodiment, in order to capture the propagation characteristics of the moving target to the signal in different frequency ranges, the Doppler spectrum is divided into multiple sub-ranges according to the frequency, and different speeds are estimated respectively, and the motion characteristics at different scales are observed, so as to further enhance the sensitivity of spectrum feature extraction and adaptability to dynamic environments. The multi-scale speed calculation formula is:

[0126]

[0127] Among them, f d represents the Doppler frequency, f t represents the signal frequency at time t, and c represents the speed of light.

[0128] In this embodiment, the frequency sub-ranges are divided into three, and three scale speeds are calculated according to formula (5), namely: scale 1 speed, scale 2 speed and scale 3 speed.

[0129] like Figure 2As shown, in step S4, the shadow fading area is detected according to the shadow fading parameters; it is determined whether the moving target enters the shadow fading area. If it is in the shadow fading area, the attenuation speed is reconstructed to obtain the reconstructed scale speed. If not, no reconstruction is performed, and the scale speed is not reconstructed.

[0130] S4-1. Use the shadow fading parameter SFP to detect whether there is a shadow fading area.

[0131] Calculate the number of SFP=1 in the sliding window. When the number of 1 in the window exceeds the threshold, it indicates that the shadow fading area has been entered. When the number of 1 in the window is less than the threshold, it indicates that the shadow fading area has been left. The calculation formula is:

[0132]

[0133] According to formula (6), the range of the shadow fading area is obtained.

[0134] In this embodiment, it is detected that shadow attenuation exists in the scale 1 speed and the scale 2 speed of the moving target, and the attenuated speeds of the scale 1 speed and the scale 2 speed are reconstructed to obtain the reconstructed scale speed; if there is no shadow attenuation in the scale 3 speed of the moving target, the attenuated speed is not reconstructed to obtain the unreconstructed scale speed.

[0135] Through the above steps, it is possible to accurately locate the object entering the fading zone and accurately reconstruct the attenuation speed.

[0136] S4-2. Calculate the attenuation rate of the multi-scale velocity in the shadow fading region.

[0137] In this embodiment, for the linear motion of the moving target, when the target passes through the shadow fading area, a model related to the speed and the diffraction path is derived according to the Fresnel zone diffraction perception model, and the calculation formula is:

[0138]

[0139] Among them, λ represents the wavelength of the Wi-Fi signal, Δt represents the time to pass through the shadow fading area, and s represents the diffraction path length.

[0140] The attenuation speed of scale 1 speed and scale 2 speed with shadow attenuation is calculated according to formula (7).

[0141] S4-3. Reconstructing the decay velocity by folding the multi-scale velocity data in the shadow fading region

[0142] After the velocity attenuation of shadow fading is negatively correlated with the signal attenuation of the Fresnel zone diffraction perception model, the attenuation velocity is reconstructed by folding the velocity value in the shadow fading area. The reconstruction formula is:

[0143] v reconstruct =2*max(v SFP (t))-v SFP (t)(8)

[0144] Among them, v SFP (t) represents the velocity value at time t in the shadow fading area.

[0145] In this embodiment, according to formula (8), the attenuated speeds are reconstructed for the scale 1 speed and scale 2 speed with shadow attenuation by folding the speed values ​​in the shadow fading region to obtain the reconstructed scale 1 speed and the reconstructed scale 2 speed. The scale 3 speed does not have shadow attenuation and is still the scale 3 speed.

[0146] In order to make the reconstructed velocity values ​​continuous, the boundary positions are smoothed using the weighted average method.

[0147] Through the above steps, it is ensured that the velocity value maintains continuity and smoothness in the reconstructed signal, thereby enhancing the stability of subsequent processing.

[0148] like Figure 2 As shown, in step S5, a speed fusion algorithm with dynamic weights is used to fuse the reconstructed scale data and the unreconstructed scale data to obtain the speed of the moving target.

[0149] In this embodiment, a velocity fusion algorithm with dynamic weights is used to fuse the reconstructed scale velocity and the unreconstructed scale velocity. The specific process is as follows:

[0150] (1) Calculate multi-scale velocity characteristic parameters.

[0151] In this embodiment, the scale velocity characteristic parameter C is calculated. v , the formula is:

[0152]

[0153] Where σ represents the standard deviation of the scale velocity, Represents the mean of the scaled velocity.

[0154] According to formula (9), the characteristic parameters of the reconstructed scale 1 velocity, the reconstructed scale 2 velocity and the scale 3 velocity are calculated respectively.

[0155] (2) Calculate the correlation between multi-scale velocities.

[0156] In this embodiment, the correlation between scales is calculated as follows:

[0157] CorrMatrix=[Corr(v1,v2),Corr(v1,v3),Corr(v2,v3)](10)

[0158] Among them, v1, v2 and v3 represent the reconstruction scale 1 speed, the reconstruction scale 2 speed and the scale 3 speed respectively.

[0159] (3) Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively.

[0160] For the speed in the shadow fading area, calculate its weight ω detect , the formula is:

[0161] ω detect =1-C v (11)

[0162] Among them, C v Represents the scale velocity characteristic parameter.

[0163] In this embodiment, the weights of the scale 1 speed and the scale 2 speed in the shadow fading area are calculated by formula (11).

[0164] At the same time, for the speed without shadow fading, calculate its weight ω undet3ct , the formula is:

[0165]

[0166] In this embodiment, the weight of the scale 3 speed in the absence of shadow fading area is calculated by formula (12).

[0167] (4) According to the reconstructed multi-scale velocities and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

[0168] In this embodiment, the vectors of the reconstructed multi-scale velocities and weights are respectively expressed as:

[0169]

[0170] According to the multi-scale velocity and weight vector, the reconstructed scale velocity and the unreconstructed velocity are fused. The formula is:

[0171] v estimate =ω T v(14)

[0172] Among them, v represents the reconstructed multi-scale velocity vector, and ω represents the corresponding weight vector.

[0173] After the above steps, the multi-scale data is integrated to reduce the impact of single-scale error on the overall estimation and improve the accuracy of speed estimation. The weight parameters are dynamically adjusted to suppress the interference of abnormal scales on the overall speed estimation and improve the system's adaptability to complex environments.

[0174] In this embodiment, the speed of a moving target can be accurately measured through key technologies such as dynamic signal extraction, multi-scale spectrum analysis, shadow fading parameter estimation, and multi-scale velocity fusion. It can effectively deal with noise interference and signal attenuation problems in complex environments and is suitable for speed measurement of various target objects.

[0175] Embodiment 2

[0176] The purpose of this embodiment is to provide a moving target speed measurement system based on Wi-Fi signals, including:

[0177] Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets;

[0178] A shadow fading parameter estimation module is used to extract an automatic gain control value based on a Wi-Fi dynamic signal caused by a moving target; and calculate a shadow fading parameter according to the automatic gain control value;

[0179] The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the Wi-Fi dynamic signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity;

[0180] The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area, if it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed, if not, no reconstruction is performed, and the scale speed is not reconstructed;

[0181] The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

[0182] Based on providing a mobile target speed measurement system based on Wi-Fi signals, the method steps in embodiment 1 are implemented.

[0183] The data acquisition module includes a transmitting end and a receiving end, which are used for sending and receiving signals.

[0184] Both the transmitter and the receiver are equipped with an acquisition system platform based on the channel state information tool. The acquisition system platform is set to Monitor mode. In Monitor mode, the signal transmission rate of the transmitter device is set to 1000 Hz. The transmitter device can stably send data packets at a higher sending rate. Python and Matlab are used to extract and process the Wi-Fi CSI data in the received data packets.

[0185] The steps involved in the apparatus of the above embodiment correspond to the method embodiment 1, and the specific implementation method can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0186] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0187] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for measuring the speed of a moving target based on Wi-Fi signals, characterized in that: include: Acquire dynamic Wi-Fi signals caused by moving targets; Extract automatic gain control value based on Wi-Fi dynamic signal caused by moving targets; Calculate shadow fading parameters according to the automatic gain control value; Extract Doppler spectrum based on the dynamic Wi-Fi signal caused by moving targets; Divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity; Detect shadow fading area according to shadow fading parameters; Determine whether the moving target enters the shadow fading area. If it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed. If it is not in the shadow fading area, no reconstruction is performed and the scale speed is not reconstructed. The reconstructed scale data and the unreconstructed scale data are fused using a dynamic weighted velocity fusion algorithm to obtain the velocity of the moving target.

2. A method for measuring the speed of a moving target based on a Wi-Fi signal as claimed in claim 1, characterized in that: Obtain the Wi-Fi dynamic signal caused by the moving target. The specific process is as follows: Calculate the subcarrier performance parameters in the Wi-Fi dynamic signal, and extract the subcarrier with large dynamic response based on the subcarrier performance parameters; The dynamic signal ratio method is used to perform hardware noise suppression on subcarriers with large dynamic response; Use a Gaussian filter to perform Gaussian noise suppression on the signal after hardware noise suppression; The signal after Gaussian noise suppression is processed by adaptive filtering to obtain the Wi-Fi dynamic signal caused by the moving target.

3. A method for measuring the speed of a moving target based on a Wi-Fi signal as claimed in claim 2, characterized in that: The signal after Gaussian noise suppression is processed by adaptive filtering. The specific process is as follows: Perform linear interpolation on the signal suppressed by Gaussian noise to obtain a continuous suppressed signal; The linear phase technique is used to keep all frequency components of the continuous suppression signal with the same time delay; The Butterworth bandpass filter is used to remove irrelevant static frequency components and suppress low-frequency drift and high-frequency noise; Wavelet transform filtering is used to perform fine-grained dynamic signal extraction in each frequency band to obtain the Wi-Fi dynamic signal caused by the moving target.

4. A method for measuring the speed of a moving target based on a Wi-Fi signal as claimed in claim 1, characterized in that: Extract the automatic gain control value by parsing the automatic gain control value using the Python or MATLAB script of the Channel State Information Tool.

5. A method for measuring the speed of a moving target based on Wi-Fi signals as claimed in claim 1, characterized in that: Extract the Doppler spectrum. The specific process is: The main components are extracted from the Wi-Fi dynamic signals caused by moving targets through singular value decomposition technology; The Doppler spectrum is extracted from the principal components using short-time Fourier transform.

6. A method for measuring the speed of a moving target based on Wi-Fi signals as claimed in claim 1, characterized in that: Determine whether there is shadow attenuation in the multi-scale velocity. If there is shadow attenuation, reconstruct the attenuation velocity. The specific process is: Calculate the decay speed of multi-scale velocities in the presence of shadow decay; The decay velocity is reconstructed by folding the multi-scale velocity data with shadow attenuation.

7. A method for measuring the speed of a moving target based on Wi-Fi signals as claimed in claim 1, characterized in that: The speed fusion algorithm with dynamic weights is used to fuse the reconstructed scale speed and the unreconstructed scale speed to obtain the speed of the moving target. The specific process is as follows: Calculate multi-scale velocity characteristic parameters; Calculate the correlation between scales of multi-scale velocities; Based on the correlation calculation, the weights of the reconstructed scale velocity and the unreconstructed scale velocity are calculated respectively; According to the reconstructed multi-scale velocities and the corresponding weights, the reconstructed scale velocity and the unreconstructed scale velocity are fused to obtain the velocity of the moving target.

8. A moving target speed measurement system based on Wi-Fi signals, characterized in that: include: Data acquisition module, used to obtain Wi-Fi dynamic signals caused by moving targets; A shadow fading parameter estimation module is used to extract an automatic gain control value based on a Wi-Fi dynamic signal caused by a moving target; and calculate a shadow fading parameter based on the automatic gain control value; The multi-scale spectrum analysis module is used to extract the Doppler spectrum based on the Wi-Fi dynamic signal caused by the moving target; divide the Doppler spectrum into multiple scales and calculate the multi-scale velocity; The shadow fading area detection module is used to detect the shadow fading area according to the shadow fading parameters; determine whether the moving target enters the shadow fading area, if it is in the shadow fading area, reconstruct the attenuation speed to obtain the reconstructed scale speed, if not, no reconstruction is performed, and the scale speed is not reconstructed; The moving target speed measurement module is used to fuse the reconstructed scale data and the unreconstructed scale data using a dynamic weighted speed fusion algorithm to obtain the speed of the moving target.

9. A mobile target speed measurement system based on Wi-Fi signals as claimed in claim 8, characterized in that: The data acquisition module includes a transmitting end and a receiving end, which are used for sending and receiving signals.

10. A mobile target speed measurement system based on Wi-Fi signals as claimed in claim 8, characterized in that: Both the transmitting end and the receiving end are provided with a collection system platform based on a channel state information tool, and the collection system platform is set to a Monitor mode.

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